Prediction of Disease Transmission Risk in Universities Based on SEIR and Multi-hidden Layer Back-propagation Neural Network Model  

作　　者：Jiangjiang Li Lijuan Feng 

机构地区：[1]School of Electronic and Electrical Engineering,Zhengzhou University of Science and Technology,Zhengzhou 450064 China

出　　处：《IJLAI Transactions on Science and Engineering》2024年第1期24-31,共8页IJLAI科学与工程学报汇刊（英文）

基　　金：supported by Key research Project of higher education institutions in Henan Province(Project:Name:A Study on Students’concentration in Class Based on Deep Multi-task Learning Framework,Project No.23B413004);the Science and Technology Project No.222102310222.

摘　　要：Against the background of regular epidemic prevention and control,in order to ensure the return of teachers to work,students to return to school and safe operation of schools,the risk of disease transmission is analyzed in key areas such as university canoons,auditoriums,teaching buildings and dormitories.The risk model of epidemic transmission in key regions of universities is established based on the improved SEIR model,considering the four groups of people,namely susceptible,latent,infected and displaced,and their mutual transformation relationship.After feature post-processing,the selected feature parameters are processed with monotone non-decreasing and smoothing,and used as noise-free samples of stacked sparse denoising automatic coding network to train the network.Then,the feature vectors after dimensionality reduction of the stacked sparse denoising automatic coding network are used as the input of the multi-hidden layer back-propagation neural network,and these features are used as tags to carry out fitting training for the network.The results show that the implementation of control measures can reduce the number of contacts between infected people and susceptible people,reduce the transmission rate of single contact,and reduce the peak number of infected people and latent people by 61%and 72%respectively,effectively controlling the disease spread in key regions of universities.Our method is able to accurately predict the number of infections.

关 键 词：Disease transmission SEIR model PREDICTION Stacked sparse denoising automatic coding network 

分 类 号：H31[语言文字—英语]